English grammar intelligent error correction technology based on the n-gram language model

Abstract

Abstract With the development of the Internet, the number of electronic texts has increased rapidly. Automatic grammar error correction technology is an effective safeguard measure for the quality of electronic texts. To improve the quality of electronic text, this study introduces a moving window algorithm and linear interpolation smoothing algorithm to build a Cn-gram language model. On this basis, a syntactic analysis strategy is introduced to construct a syntactic error correction model integrating Cn-gram and syntactic analysis, and English grammar intelligent error correction is carried out through the model. The results show that compared with the Bi-gram and Tri-gram, the precision of the Cn-gram model is 0.85 and 0.91% higher, and the F1 value is 0.97 and 1.14% higher, respectively. Compared with the results of test set Long, the Cn-gram model has better performance on verb error correction of the Short test set, and the precision rate, recall rate, and F1 value are increased by 0.86, 3.94, and 1.87%, respectively. The comparison of the precision, recall rate, and F1 value of the proposed grammar error correction model on the complete test set shows that the precision of the study is 19.10 and 5.41% higher for subject–verb agreement errors. The recall rate is 9.55 and 10.77% higher, respectively; F1 values are higher by 12.65 and 10.59%, respectively. The above results show that the error-correcting technique of the research design has excellent error-correcting performance. It is hoped that this experiment can provide a reference for the relevant research of automatic error correction technology of electronic text.